{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ab882c9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.functional as F\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ca6bf78e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.141592653589793"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pi的值\n",
    "torch.pi"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b35e1a5c",
   "metadata": {},
   "source": [
    "# 模拟sin函数\n",
    "\n",
    "y=sin(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6f97fe00",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义目标函数\n",
    "def target_function(x):\n",
    "    return torch.sin(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4cadeb47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.6454, 0.2797, 0.4175],\n",
       "        [0.3067, 0.9714, 0.6263],\n",
       "        [0.2962, 0.0383, 0.8169]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.rand(3,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4f2e5cb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = torch.linspace(-2*torch.pi, 2*torch.pi, 1000)\n",
    "y_train = target_function(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1949be65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2d45b33b250>]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fHKgGAHwnRx03VgC4ZbIn1k8P1bAGgFTvg5JKAMDVoxMUs3LxxQyNi0DOUCvcAvDRPi7bQOrHBCfIbDlWhxaHEykWE6YMi5E7nAGbPDQGQ6zhaHU48fkR7m5M6iUIAj7Y60lwbs5OlTmawfn2RE+8H3rjJ1IzJjhBRryxfmtiCrRajczRDJxGo8G3JqUAAD7cx5srqdf+ChvONLTDFKZF/tgkucMZlG9NTIFWAxSXNnOPOFI9JjhBpM3hRMFhzwyImyapZ3hKJLYeCw7Xcl8cUq1/eYenvjkuGZFGvczRDE6i2YQrM+MAsKFB6scEJ4j853ANOrvdyIiLwPghZrnDGbQrUs3ITIiEw+nGpkPVcodDNGgut4AN3sTgpknqGp4SiXGLdUREahWQBGfdunXIyMiAyWRCXl4eCgsLL3jsrFmzoNFoznvceOONvmPuuuuu8/4+d+7cQFyKook3pJsmpUKjUc/wlEij0ZxTA8AiR1KfwtONqLE7YDbpcfXoeLnDuSTXj09BmE6DI9UtOF7TInc4RJdM8gTnzTffxMqVK7FmzRoUFxdj0qRJmDNnDmpr+y4kfffdd1FVVeV7HDhwADqdDrfeemuv4+bOndvruH/84x9SX4qiNbd3YevxOgDwTblWIzH2rcfq0NzeJXM0RIMj9t5cPz4FRr3yl2joiyUiDNeMTgDQU9NHpEaSJzhPPfUUlixZgkWLFmHcuHFYv349IiIi8OKLL/Z5fGxsLJKTk32PTZs2ISIi4rwEx2g09jouJkY9M4aksOlQDbpdArKSozEqKVrucC7ZyMQojEsxw+kW8MlBDlORerjcAj7xbs1w48QUmaO5PN/y9qRuPMDvIKmXpAlOV1cXioqKkJ+f3/OGWi3y8/Oxffv2AZ3jhRdewPz58xEZGdnr+c2bNyMxMRFjxozBsmXL0NBw4RVwHQ4H7HZ7r0ewEW+sali5+GJumOC5BvGaiNRgT2kT6lsdiDbpfYW6anVtViL0Wg2O17biZF2r3OEQXRJJE5z6+nq4XC4kJfWeKpmUlITq6ou3DAoLC3HgwAHcfffdvZ6fO3cuXnnlFRQUFOB3v/sdtmzZguuvvx4uV98LxK1duxYWi8X3SE9X/v5Mg9He5cQX3uGpuePVn+Bc503Svjxez9lUpBpib0f+2CQY9Oqev2EJD8OMkZ4aIvakklop+lv4wgsvYMKECZg2bVqv5+fPn4+bbroJEyZMwLx587Bhwwbs2rULmzdv7vM8q1atgs1m8z3KyoJrr5UtR+vgcLoxNDYCWcnqHZ4SjUqMwvD4SHS53Nh8lIv+kfIJgoBPvDP/gqEXFQDmXOFpmLInldRK0gQnPj4eOp0ONTW9vyA1NTVITu7/JtDW1oY33ngDixcvvuj7ZGZmIj4+HidOnOjz70ajEWazudcjmIgtrDlXJKly9tTXaTQaXOe9uX7KmyupwKEqO8oaO2AK0/oKdNXum+OSoNEAe8uaUWXrkDscokGTNMExGAzIzc1FQUGB7zm3242CggJMnz6939e+/fbbcDgcuOOOOy76PuXl5WhoaEBKiroL+y5Fl9ONAu/WBsHScgR6ruXzI7Xocrpljoaof+L+b9eMTlDFBrcDkRhtQu5Qz+QNNjRIjSQfolq5ciWee+45vPzyyzh8+DCWLVuGtrY2LFq0CACwcOFCrFq16rzXvfDCC5g3bx7i4noX67W2tuJnP/sZduzYgTNnzqCgoAA333wzRo4ciTlz5kh9OYqz41QDWjqdiI8yYvLQ4JlJlp1mRWK0ES0OJ746WS93OET9CqYi/3OJ18PZVKRGkic4t99+O5588kmsXr0a2dnZKCkpwcaNG32Fx6Wlpaiq6r2o29GjR/Hll1/2OTyl0+mwb98+3HTTTRg9ejQWL16M3NxcfPHFFzAajVJfjuKIw1PfHJekqr2nLkar1eCb41gDQMp3ur4NR2taoNdqMDtLXXtPXYyY4BSeaURjG9elInUJyEYpy5cvx/Lly/v8W1+FwWPGjIEgCH0eHx4ejk8++cSf4amW2y3g00NiyzG4bqyA5+b62s5SbDpUg9/MGx9UCRwFD3H/tysz42CJCJM5Gv8aGheBsSlmHK6y47MjtfhebprcIRENmKJnUVH/DlbaUdfiQKRBh+kj1L3uRl+uzIxDtFGP+lYH9lXY5A6HqE+feWvg/isrUeZIpJE/1nNdnx/hjEZSFyY4KibeWGeOilftsvD9Mei1+IZ3P5/PeHMlBWrp7Ebh6UYAwZvgXOu9rq3H6tDtYsE/qQcTHBX77GhwtxwB4NoxnmvjejikRF8cr4fTLSAzPhIZ8ZEXf4EKTUqzIi7SgBaHE7vPNMkdDtGAMcFRqfpWB/aVNwMAZo0J3gTnmjGeNUX2ldtQ29IpczREvYk9i9cGcSNDp9X4voefs6FBKsIER6W2HK2DIABXpJqRZDbJHY5kEqNNmDDEAsBzzURK4XYLvp7FYO5FBXquTyyoJlIDJjgqJQ5PXRvEvTcisXXM1iMpyf4KG+pbuxBl1GNqRqzc4UjqG6MSoNNqcLKuDaUN7XKHQzQgTHBUqNvlxtZjnt6MYO4aF13r7R7/4lg9ixxJMXxF/iPjVb+55sVYwsMwZZhnIdHPjrAXh9QhuL+VQar4bBNaOp2IiQhDdrpV7nAkxyJHUqLPQ2R4SjTbO138Mw4Vk0owwVEhcXjqmtGebuNgp9VqfBsYcjYVKUFtSyf2lXvWZpqVFRyba16MmMjtONmA9i6nzNEQXRwTHBX6PARmbnzdLO+1cj0cUgKx4H3CEAsSo4O3yP9cIxKiMMQaji6XGzu9a/8QKRkTHJWpaO7AsZpWaDXw9WqEgmtGJUCrAY7XtqKskUWOJK+txz0bwM4aEzrfQY1Gg6u9C29+cYwb4JLyMcFRmS+Pe1qOk9KtsEYYZI4mcCwRYb7d0r84zpsrycftFrDthOe/wW+MCp0EB+i53i+Osw6HlI8JjsqIP+6hdmMFPFtSAMCXJ3hzJfkcqrKjsa0LkQYdcoZa5Q4noGaMiPP1pFY2d8gdDlG/mOCoSO+WY7zM0QSemNRtO9EAl7vv3eaJpCY2MqaPiEeYLrRuodYIAyamWQEAX7InlRQutL6dKnew0o6m9m5EGfUhMT386yalWRBt0sPW0Y0D3F2cZCIOz4RiIwMArvbW/m3lMBUpHBMcFfnCOzRzZWZsyLUcAUCv02J6ZhwA4MsTbD1S4HV0uXxrMYVsguMbKq5nTyopWuj9SqrYlyFcfyMSf1RY5Ehy2Hm6AV0uN4ZYwzE8SHcPv5hJ6VZEG/Vobu/GwUr2pJJyMcFRiXNbjjNDtOUIADO9yV3R2Sa0ObjYGAVWT5F/PDSa4F9ksy9hOi2mj/D0pIpbxhApERMclRBbjqkWEzJDtOUIABlxERhiDUe3S0AhFxujAGMvqsc3fHU4HCom5WKCoxLn3lhDteUIeBYb6xmm4s2VAqfG3omjNS3QaICrRsbJHY6sxDqc4rNNaGVPKikUExyVEItqQ3l4SsT1cEgOYkI9cYglpBbZ7MuwuEgMjY2A0y2g8HSD3OEQ9YkJjgrU2jtxpFpsOTLBuWpEPDQa4FhNK2rsnXKHQyFiGxsZvczw1uFsP8kEh5SJCY4KfOW9gVyRakZsZGi3HAEgJtKACUMsALjYGAWGIAi+H/KrRjDBAeArNP6KCQ4pFBMcFRBvrDN4Y/URP4vtp3hzJemdaWhHtb0TBp0Wk4fFyB2OIohrUh2qsqO5vUvmaIjOxwRHBcQfcfGGQj2txx1McCgAvjrp6SnMGWqFKUwnczTKkGg2YURCJAQB2HGKMxpJeZjgKFxFcwdKG9uh02owJYMtR9GUYTHQazUob+pAWWO73OFQkBN7UcXEmjzEnlQ2NEiJmOAo3A7vjXX8EAuiTWEyR6MckUY9JqZ56nA4TEVSEgTB10PBXtTeprPQmBSMCY7CcXjqwjhMRYFworYV9a0OGPVaZA+1yh2OolzpvS8drWlBfatD5miIemOCo3DsGr8w8ea642QDBIGb/pE0xEbGlIwYGPWsvzlXbKQBWcnRANjQIOVhgqNgZY3tqGjugF6rwRTO3DhP7rAYhOk0qLR1opR1OCQRXyODvah94nRxUiomOAomthwnplkQadTLHI3yRBj0mJRmBcDWI0nD7RZ8/22xF7VvvkJjJjikMAFJcNatW4eMjAyYTCbk5eWhsLDwgse+9NJL0Gg0vR4mk6nXMYIgYPXq1UhJSUF4eDjy8/Nx/PhxqS8j4HZweOqiWORIUjpa04Km9m5EGHSY6E2mqbdpw2Oh1QCn6ttQbePK4qQckic4b775JlauXIk1a9aguLgYkyZNwpw5c1BbW3vB15jNZlRVVfkeZ8+e7fX3xx9/HE8//TTWr1+PnTt3IjIyEnPmzEFnZ/B8uQRBOKfAmAv8XYg4bLD9FOtwyP/ExHlKRizCdOzw7oslPAzjh4gzGrmyOCmH5N/Yp556CkuWLMGiRYswbtw4rF+/HhEREXjxxRcv+BqNRoPk5GTfIykpyfc3QRDwhz/8Ab/4xS9w8803Y+LEiXjllVdQWVmJ999/X+rLCZizDe2osnUiTKdBLutvLmjysBgYdFrU2B0408A6HPKvr1h/MyC+hgZ7UklBJE1wurq6UFRUhPz8/J431GqRn5+P7du3X/B1ra2tGDZsGNLT03HzzTfj4MGDvr+dPn0a1dXVvc5psViQl5d3wXM6HA7Y7fZeD6UTx/2z060IN3DmxoWYwnS+qbu8uZI/udwCdp7mMPFAXDmipyeVSCkkTXDq6+vhcrl69cAAQFJSEqqrq/t8zZgxY/Diiy/iX//6F1599VW43W7MmDED5eXlAOB73WDOuXbtWlgsFt8jPT39ci9Nclz/ZuDEz4iFxuRPh6vsaOl0Isqox/hUs9zhKNqUYTHQaoCyxg7W4ZBiKG5Qefr06Vi4cCGys7NxzTXX4N1330VCQgL+8pe/XPI5V61aBZvN5nuUlZX5MWL/86yc6vmxvpItx4u6knU4JIGdpz2rF0/JiIGe9Tf9ijaFYZw3CSw8w32pSBkk/dbGx8dDp9Ohpqam1/M1NTVITk4e0DnCwsKQk5ODEydOAIDvdYM5p9FohNls7vVQstLGdtTYHQjTaTB5KOtvLiZnqBUGvRZ1LazDIf/Z5U1wpg2PlTkSdZia4fmcCk+zJ5WUQdIEx2AwIDc3FwUFBb7n3G43CgoKMH369AGdw+VyYf/+/UhJSQEADB8+HMnJyb3OabfbsXPnzgGfU+kKvTfWCUMs3Ll4AExhOkzy7ksl/igRXQ5BELDL2xMxLYMJzkDkeRPBXaebZI6EyEPyfteVK1fiueeew8svv4zDhw9j2bJlaGtrw6JFiwAACxcuxKpVq3zHP/zww/j0009x6tQpFBcX44477sDZs2dx9913A/DMsFqxYgUeffRRfPDBB9i/fz8WLlyI1NRUzJs3T+rLCQjxxjqVLccB87Ue2T1OfnCqvg0NbV0w6LWY4E2eqX9TvN/BozUtaGrrkjkaIkDy5XFvv/121NXVYfXq1aiurkZ2djY2btzoKxIuLS2FVtuTZzU1NWHJkiWorq5GTEwMcnNz8dVXX2HcuHG+Yx544AG0tbVh6dKlaG5uxsyZM7Fx48bzFgRUq11nPC0gthwHburwWGDzSV9ySHQ5xF7U7HQr958aoPgoI0YkROJkXRt2n23CN8clXfxFRBLSCCFYlWm322GxWGCz2RRXj1Pb0olpvymARgOU/PI6WCLC5A5JFeyd3Zj0608hCEDhz2cj0RwcyS7JY+WbJXh3TwWWXzsS/zNnjNzhqMaqd/fhH4VlWPKN4fjfG8dd/AVEgzSY329ODVCY3d7emzFJ0UxuBsFsCsPYZM7iIP8Q/xtigfHgiJ9X4RnW4ZD8mOAojNg1PpXDU4M2zVfkyASHLl1lcwfKmzqg1XhWyqaBE+9bBypsaHM4ZY6GQh0THIVhgfGl6yk0ZuuRLp34Hbwi1YIoo+RlikElLSYCQ6zhcLkFFJfye0jyYoKjIC2d3Thc5dlGggXGgzd1uKe1faTaDltHt8zRkFqxF/XyTM3wfA/Zk0pyY4KjIEVnm+AWgPTYcCRbWCQ7WInRJmTERUAQgOKzbD3SpdnF+pvLMm24Z2XxnUxwSGZMcBTENzzFluMlm8L1cOgyNLV14VhNK4CengganGnentSSsmY4nC6Zo6FQxgRHQcQVQDk8denEz47d43QpxEbGiIRIxEUZZY5GnUYkRCE20gCH04395Ta5w6EQxgRHIRxOF0rKmwGwwPhyiJ/dvnIbOrvZeqTB4fDU5dNoNL7eL/akkpyY4CjEvnIbupxuxEcZkBkfKXc4qpURF4H4KCO6XG7sLWuWOxxSGXEGHoeJL49Yh1PInlSSERMchRBvBFOGxUKj0cgcjXppNBpfDQC3baDBaHM4cbDCM6TCHpzLIw4VF51tgtsdcovlk0IwwVEIrn/jP1wPhy5FSVkznG4BqRYT0mIi5A5H1camRCM8TIeWTieO17bKHQ6FKCY4CuByCyg6ywJjfxETnOKzTXCx9UgDJH4Hp/A7eNn0Oi2y060Aej5XokBjgqMAx2tb0NLpRIRBh7Ep0XKHo3pjU8yIMurR6nDiaHWL3OGQSog/xLncnsEvpngLjXef5VAxyYMJjgIUn20GAGSnW6HX8V/J5dJpNcgZagUAFHG5eBoA9zlbCzDB8Q9xHy8uukly4a+pArDl6H+Th/LmSgN3oq4VLZ1OhIfpkJXMXlR/mJzu+Q6eaWhHfatD5mgoFDHBUQCx5cidi/1HTBY5/k8DIf53wl5U/7FEhGF0UhQAfg9JHvwmy6yh1YHT9W0Aelo8dPmyh1qh0QClje2obemUOxxSOPaiSiOXw1QkIyY4MisubQYAjEqMgiUiTN5ggojZFIYxSZ6hBrHGiehCipngSEIcKmYPDsmBCY7M2HKUjq/IkYXG1I/Gti6c8vaiisXp5B/ilPt9FTZuvEkBxwRHZmLLkfU3/pfL1iMNgPgdHJkYBWuEQeZogktGXARiIw3ocrpxoMIudzgUYpjgyKjL6cZe7wab7MHxP/Ez3V/O1iNdmLiUgJgQk/9oNBrOaCTZMMGR0eEqOxxON6wRYdxgUwLD4iIQF2lAl4utR7owDhNLiwv+kVyY4MhIvLFOHhrDDTYloNFouNgY9av7nF3nOUwsjZ4lG5ohCNw6hQKHCY6MirhyquS4Hg7151Ale1GlNmGIBWE6DepbHShr7JA7HAohTHBkVHxODw5Jw5fglDax9UjnObcXVatlL6oUTGE6jB9iAcBhKgosJjgyqWzuQJWtEzqtBpPSLXKHE7TE1mNdiwPlTWw9Um/sRQ0MzmgkOTDBkYn4RR+XYkaEQS9zNMHLFKbDFameBJI3V/o69qIGhlhozO8gBRITHJlw5kbgsA6H+sJe1MARE8ijNS2wd3bLHA2FCiY4MuEGm4HD5eKpL+xFDZxEswnpseEQBGCPd3saIqkxwZFBR5cLhyo967KwB0d6k4dZAQBHqu1odTjlDYYUg72ogSXW4ezh1ikUIExwZLCvvBlOt4BkswmpFpPc4QS9FEs4hljD4RbgW/OEiL2ogZXjS3Ca5Q2EQkZAEpx169YhIyMDJpMJeXl5KCwsvOCxzz33HL7xjW8gJiYGMTExyM/PP+/4u+66CxqNptdj7ty5Ul+G35w7c4ML/AWG+CO2+wxbjwS0dzlxkL2oASVuZFpS1gy3m0s2kPQkT3DefPNNrFy5EmvWrEFxcTEmTZqEOXPmoLa2ts/jN2/ejO9///v4/PPPsX37dqSnp+O6665DRUVFr+Pmzp2Lqqoq3+Mf//iH1JfiN9xgM/ByfTdXJjgE7Cu3weUWkGQ2shc1QLKSzTDqtbB1dON0Q5vc4VAIkDzBeeqpp7BkyRIsWrQI48aNw/r16xEREYEXX3yxz+Nfe+01/OhHP0J2djaysrLw/PPPw+12o6CgoNdxRqMRycnJvkdMjDqSBUEQUOztop3s/dEl6fm6x8u4XDx5ehEAbpMSSAa9FhO8C/5xmIoCQdIEp6urC0VFRcjPz+95Q60W+fn52L59+4DO0d7eju7ubsTGxvZ6fvPmzUhMTMSYMWOwbNkyNDQ0XPAcDocDdru910MupY3taGzrgkGv9a3PQtIbm2KGQa9Fc3s3zjS0yx0OyazE+wObnW6VNY5QIw5TsdCYAkHSBKe+vh4ulwtJSUm9nk9KSkJ1dfWAzvHggw8iNTW1V5I0d+5cvPLKKygoKMDvfvc7bNmyBddffz1cLlef51i7di0sFovvkZ6efukXdZnElssVqZ4fXAoMg16L8almABymImCP978BJjiBxUJjCiRF/8L+9re/xRtvvIH33nsPJlPPOPn8+fNx0003YcKECZg3bx42bNiAXbt2YfPmzX2eZ9WqVbDZbL5HWVlZgK7gfGLXOG+sgcebKwFAla0DNXYHdFoNJqSxFzWQxB6cI9V2tHdxyQaSlqQJTnx8PHQ6HWpqano9X1NTg+Tk5H5f++STT+K3v/0tPv30U0ycOLHfYzMzMxEfH48TJ070+Xej0Qiz2dzrIZc93gQnh0vDB5yYVDLBCW3i8NSYpGgu8BdgKZZwJJtNcAvA/nKb3OFQkJM0wTEYDMjNze1VICwWDE+fPv2Cr3v88cfxyCOPYOPGjZgyZcpF36e8vBwNDQ1ISUnxS9xS6ex24VCl50udwx6cgBNbj4er7Ojs7ns4k4KfrxeVRf6y8NXhcE0qkpjkQ1QrV67Ec889h5dffhmHDx/GsmXL0NbWhkWLFgEAFi5ciFWrVvmO/93vfodf/vKXePHFF5GRkYHq6mpUV1ejtbUVANDa2oqf/exn2LFjB86cOYOCggLcfPPNGDlyJObMmSP15VyWQ1V2dLsExEUakBYTLnc4IWeINRwJ0UY43QIOVLD1GKr2sMBYViw0pkCRPMG5/fbb8eSTT2L16tXIzs5GSUkJNm7c6Cs8Li0tRVVVle/4Z599Fl1dXfje976HlJQU3+PJJ58EAOh0Ouzbtw833XQTRo8ejcWLFyM3NxdffPEFjEaj1JdzWcSu8ZyhVk5NlYFGo/H1nHGYKjQ5XW7sr2AvqpzE4fniUi7ZQNIKyAD08uXLsXz58j7/9vXC4DNnzvR7rvDwcHzyySd+iiyw9rDAWHbZQ6349FCNbxYNhZajNS3o6HYh2qjHiIQoucMJSeNTLdBrNahrcaDS1okhVvZmkzQUPYsq2JT4pqaywFguOd7PvoQ9OCFJrL+ZlG6FVsteVDmEG3QYm+KZ6MFhKpISE5wAqW91oKyxAxoNMDGdU1PlMjHNAq0GqLR1otrWKXc4FGCsv1GGnjqcZlnjoODGBCdAxB6DkQlRMJvC5A0mhEUa9RiTzAX/QhXXoVIGFhpTIDDBCRDeWJXDtx4Op6mGFHtnN07WeWZjcoq4vMSh4gOVdjicXLKBpMEEJ0C49oZysHs8NO0rs0EQgPTYcMRHKXvGZbAbFheBmIgwdDndOFzVInc4FKSY4ASA2y1gr7iCMQuMZSfu4r6vvBlOl1veYChgxOEQFvnLT6PRnLN1CoepSBpMcALgZF0rWhxOhIfpMDqJU1PllhkfhWiTHp3dbhypZusxVHCYWFm4JhVJjQlOAIi1HhPSLNDr+JHLTavV+H7kSliHExIEQWCCozC+HhwW+5NE+GsbACW+DTatssZBPdh6DC3lTR1oaOtCmE6DK1Ll22yXekxMt0CjAcoaO1DX4pA7HApCTHACQPwR5dLwysHWY2gp9tZ5jEsxwxSmkzkaAgCzKQyjEj1D9uxJJSkwwZFYe5cTR6vtAFjcqCSTvMnmqbo22Nq75Q2GJMfhKWUSJ12w0JikwARHYvvLbXALQIrFhGSLSe5wyCs20oCMuAgAQEl5s7zBkOS4TIMycckGkhITHIlxg03l4jTV0OBwunCw0tOLymUalEX8Du4tb4bLzZ3Fyb+Y4EishHvfKFY2C41DwuGqFnQ53YiJCMMwb68dKcPIxChEGfVo73LhKJdsID9jgiMxjv0rl9g9XlLWDEFg6zFYlXh76CalW6HRcAdxJdFpNZiY5tl8mIXG5G9McCRUZetAtb0TOq0GE9K4g7jSZCWbYdRrYevoxun6NrnDIYmwkaFsPQ0NDhWTfzHBkZA4PDUmKRoRBr28wdB5DHotJgzxJJ4cpgpePetQsf5GicTZpezBIX9jgiMhztxQPq5oHNwa27pwpqEdAJCdZpU3GOqT+B08XtuKlk4u2UD+wwRHQlzgT/nEVn0xZ1IFJXGT28z4SFgiwuQNhvqUEG1EWkw4BAHYV26TOxwKIkxwJOJ0ubG/wvNl5RYNyiX+uzlS3YKOLpe8wZDfcZkGdeCSDSQFJjgSOVrTgo5uF6JNemTGcwdxpUqxmJAYbYTLLeBAJVuPwYb7wKkDh4pJCkxwJHLuzA2tllNTlUqjOWdncRYaBxW3W/BNEec2Kcp2boLDJRvIX5jgSGQPF/hTDbEInBtvBpfTDW2wdzph1GuRlRItdzjUjytSzQjTaVDf2oXypg65w6EgwQRHIlx7Qz3E5fvZgxNcxH+f44dYEKbjrU7JTGE6jEsxA+ipmyK6XPzWS8DW0Y0Tta0AmOCowcQ0C7QaoNLWiVp7p9zhkJ/46m/4HVQFFhqTvzHBkcA+7+7UQ2MjEBdllDcYuqhIox6jkzxDGGw9Bg9xyJHrUKkDC43J35jgSIAbbKqPOMuGKxoHh85uF45UeTZv5PdQHcR/Twcr7HA4uWQDXT4mOBJg/Y369LQe2T0eDA5U2OB0C4iPMmKINVzucGgAhsVFICYiDF0uNw5XcWdxunxMcPxMEATfMAfX3lAPcRrxvnIbXG5OU1W7cxsZ3EFcHXov2cCGBl0+Jjh+VtbYgca2Lhh0WoxLNcsdDg3QyMQoRBp0aO9y4VgNW49q59smhY0MVfEVGrMOh/yACY6fiYWNY1PNMOp1MkdDA6XTajCJRY5BgzOo1ImFxuRPAUlw1q1bh4yMDJhMJuTl5aGwsLDf499++21kZWXBZDJhwoQJ+Pjjj3v9XRAErF69GikpKQgPD0d+fj6OHz8u5SUMGDfYVC+uaBwcals6UdHcAY0GmJBmkTscGgSxkXG2oR0NrQ55gyHVkzzBefPNN7Fy5UqsWbMGxcXFmDRpEubMmYPa2to+j//qq6/w/e9/H4sXL8aePXswb948zJs3DwcOHPAd8/jjj+Ppp5/G+vXrsXPnTkRGRmLOnDno7JR/DRPufaNeYoLDFY3VTUxQRyVGIdrEHcTVxBIehhEJkQCAvd7lNoguleQJzlNPPYUlS5Zg0aJFGDduHNavX4+IiAi8+OKLfR7/xz/+EXPnzsXPfvYzjB07Fo888ggmT56MP/3pTwA8vTd/+MMf8Itf/AI333wzJk6ciFdeeQWVlZV4//33pb6cfjmcLhyqtAPgDCo1EtdLOV7bipbObnmDoUvmK/Ln/lOqJBb8c8kGulySJjhdXV0oKipCfn5+zxtqtcjPz8f27dv7fM327dt7HQ8Ac+bM8R1/+vRpVFdX9zrGYrEgLy/vgud0OByw2+29HlI4VGlHl8uN2EgDhsZGSPIeJJ3EaBOGWMMhCMD+cu4srla+dajYi6pKYu8363DUa8epBqx6dx8+OVgtaxySJjj19fVwuVxISkrq9XxSUhKqq/u+8Orq6n6PF/93MOdcu3YtLBaL75Genn5J13MxnJqqfj0bbzbLGgddGpdb8K0kzl5UdTq30NjNJRtUaeuxOvyjsAwFh2tkjSMkZlGtWrUKNpvN9ygrK5PkfaZmxOK+/xqJm7NTJTk/SU8sDmf3uDqdqG1FW5cLEQadb/sNUpes5GiYwrRo6XTiVH2r3OHQJdjra2TIO0ysl/Lk8fHx0Ol0qKnpncXV1NQgOTm5z9ckJyf3e7z4vzU1NUhJSel1THZ2dp/nNBqNMBql3xNq/BALxg/hrA01O7d7XBAE9sSpjLhR48Q0C3Ra/rtTI71Oi4lDrCg804g9pc0YmchEVU3cbgH7yjxD/HL3okrag2MwGJCbm4uCggLfc263GwUFBZg+fXqfr5k+fXqv4wFg06ZNvuOHDx+O5OTkXsfY7Xbs3LnzguckGqgrUi3QazWob3WgorlD7nBokHqGiVlgrGYcKlavk3WtaHE4ER6mw+ikKFljkXyIauXKlXjuuefw8ssv4/Dhw1i2bBna2tqwaNEiAMDChQuxatUq3/E/+clPsHHjRvz+97/HkSNH8Ktf/Qq7d+/G8uXLAXiW816xYgUeffRRfPDBB9i/fz8WLlyI1NRUzJs3T+rLoSBnCtNhbIpnBWoOU6kP94ELDjlck0q1xKR0whAL9Dp5q2AkHaICgNtvvx11dXVYvXo1qqurkZ2djY0bN/qKhEtLS6HV9nwIM2bMwOuvv45f/OIX+PnPf45Ro0bh/fffx/jx433HPPDAA2hra8PSpUvR3NyMmTNnYuPGjTCZTFJfDoWAnKFW7K+woaSsGd+exHoqtWh1OH3bbHAdKnUTe3CO1rSgvcuJCIPkP1XkJ3vFRoYCvoMaQRBCrkzdbrfDYrHAZrPBbOZ+UdTbu8XlWPnWXuQOi8E7y2bIHQ4N0Fcn6/GD53Yi1WLCV6tmyx0OXaYrHytAtb0Tby69EnmZcXKHQwN049Nf4GClHet+MBk3Tky5+AsGaTC/3yExi4poMMThjQMVNnQ53fIGQwNWoqCWI10+7kulPh1dLhyp9vSiKuF7yASH6GuGx0fCEh4Gh9ONI9XSLApJ/udb4I/1N0HBV2jMOhzVOFBpg8stICHaiFSL/CUjTHCIvkaj0bD1qDKCIHAGVZDJ4XdQdc5tZChhiQ0mOER94M7i6lJp60RtiwM6rQYTuBZVUJjgXcuo2t6JKhuXbFADpc1iZIJD1Aeuw6EuYiKalRyNcINO3mDILyIMeozxrkbNhoY6iAmOUmYxMsEh6kN2mhUAcLq+Dc3tXfIGQxdVUuZZwVgpLUfyj2xuvKkatfZOVDR3QKMBJnrvn3JjgkPUh5hIA4bHRwLgzVUNxELUnKGsvwkm2dwbTjXE3u7RidGIMipj3SImOEQXwEJjdeh2ubG/Qhl735B/Tfb24OyraIbTxSUblExpw1MAExyiC2LrUR2OVLXA4XTDbNIj09vrRsEhMz4K0SY9OrvdvvVVSJnEjW6V1MhggkN0AWJLZG+5Z2dxUiax/mZSuhVa7iAeVLRaLtmgBi63gP3l3l5U9uAQKV9WshkGvRbN7d0409Audzh0AeLYf46CWo7kP+xJVb7jtS1o63Ih0qDDqMRoucPxYYJDdAEGvRbjU8WdxZtkjoYupIQFxkGtpweH30GlEpPPiWlW6BTUi8oEh6gf4qq47B5Xpub2LpyqbwPgGaKi4CMmOCfr2mDr6JY3GOqTbwVjBQ1PAUxwiPqVw3U4FE3895IRF4HYSIO8wZAk4qKMGBobAQDYy++hIpUodJiYCQ5RP8TW46FKOzq7XfIGQ+dR2tLwJA02NJSrpbMbx2qVs4P4uZjgEPUjLSYc8VEGON0CDlba5A6HvqZn7Q3W3wSznkJj1uEozf5yGwQBGGINR2K0/DuIn4sJDlE/PDuLe348OYtDWXrvIG6VNRaS1rlTxblkg7KIsxiV1nsDMMEhuih2jyvTmYZ2NLd3w6DXYmyKWe5wSELjUs0w6LRoau9GaSOXbFAS3zYpCmxkMMEhugiuw6FM4rTh8ame9YooeBn1OozzLdnQLG8w5KP0XlTeFYguYmKaBRoNUNHcgboWh9zhkJf4QycOIVJwY0+q8lQ0d6C+1QG9VoPxQyxyh3MeJjhEFxFtCsOoxCgAvLkqiRI39yPpsNBYecRGxtgUM0xhOnmD6QMTHKIB4M1VWTq7XThUaQegzK5x8r/J3plyh6q4ZINSKHl4CmCCQzQgXNFYWQ5W2uB0C4iPMiAtJlzucCgA0mLCERdpQLdLwEFvckvyUnovKhMcogEQv8D7ym1wuTlNVW7n1t9oNMrZ+4ak41mywQqADQ0l6HK6sb/Cu4M4e3CI1Gt0UjQiDDq0Opw4Wdcqdzghb4/CW44kDRYaK8eRaju6nG5YwsMwPD5S7nD6xASHaAB0Wg0meGcJsA5HfiUKXnuDpNOz6Ca/g3I7t/5Gqb2oTHCIBiibrUdFqG3pREVzBzQaYEKa8qamknQmpnuWbChv4pINcusZJrbKGkd/mOAQDVAOt2xQBLH3ZlRiFKJNYfIGQwFlNoVhZAKXbFACpRcYA0xwiAZM/CIfq2lBm8MpbzAhzHdj5QJ/Iamn0JjDVHJpauvC6fo2AOzBIQoKSWYTUiwmuAXPbCqSh69rXMEtR5KOuHM8e1LlU1LeDAAYHh8Ja4RB3mD6wQSHaBA4i0NeLreAfd6bq5K7xkk6Yo8Bl2yQj1qK/CVNcBobG7FgwQKYzWZYrVYsXrwYra0XnmLb2NiI++67D2PGjEF4eDiGDh2KH//4x7DZereWNRrNeY833nhDykshAsAVjeV2vLYFbV0uRBp0GJUYLXc4JIPRSVFcskFm4jINSu9FlTTBWbBgAQ4ePIhNmzZhw4YN2Lp1K5YuXXrB4ysrK1FZWYknn3wSBw4cwEsvvYSNGzdi8eLF5x37t7/9DVVVVb7HvHnzJLwSIo9zVzQWBLYeA01sOU5Is0CnVebUVJKWXqflkg0yEgQBexW+RYNIL9WJDx8+jI0bN2LXrl2YMmUKAOCZZ57BDTfcgCeffBKpqannvWb8+PF45513fP88YsQI/OY3v8Edd9wBp9MJvb4nXKvViuTkZKnCJ+rThCGeH9baFgeqbJ1ItXKbgEDqmbnBAuNQlj3Uip2nG1FS1ozbpw6VO5yQcqq+DbaObhj1WmQlm+UOp1+S9eBs374dVqvVl9wAQH5+PrRaLXbu3Dng89hsNpjN5l7JDQDce++9iI+Px7Rp0/Diiy/225p2OByw2+29HkSXItygQ1ayZ2iEdTiBV+xtsSu95UjS4pIN8ik66/kOTkyzwKBXdhmvZNFVV1cjMTGx13N6vR6xsbGorq4e0Dnq6+vxyCOPnDes9fDDD+Ott97Cpk2bcMstt+BHP/oRnnnmmQueZ+3atbBYLL5Henr64C+IyIt1OPKwdXTjeK2n5mIye3BCGpdskI9435s8TPnfwUEnOA899FCfRb7nPo4cOXLZgdntdtx4440YN24cfvWrX/X62y9/+UtcddVVyMnJwYMPPogHHngATzzxxAXPtWrVKthsNt+jrKzssuOj0MUN/+ThqXsChsZGICHaKHc4JKMkswmpXLJBFsVnmwGoo5Ex6Bqc+++/H3fddVe/x2RmZiI5ORm1tbW9nnc6nWhsbLxo7UxLSwvmzp2L6OhovPfeewgL63+10ry8PDzyyCNwOBwwGs+/8RmNxj6fJ7oUYv3H/gobul1uhOmU3U0bLIq9XeO5Kmg5kvSyh1pRub8ae8qaMH1EnNzhhAR7ZzeO1bYACNIEJyEhAQkJCRc9bvr06WhubkZRURFyc3MBAJ999hncbjfy8vIu+Dq73Y45c+bAaDTigw8+gMlkuuh7lZSUICYmhkkMBURmfCSiTXq0dDpxtLoF44dwP6RAEOtvJit8aioFRna6FR/vr/bNrCPplZSqqxdVsqbn2LFjMXfuXCxZsgSFhYXYtm0bli9fjvnz5/tmUFVUVCArKwuFhYUAPMnNddddh7a2Nrzwwguw2+2orq5GdXU1XC4XAODDDz/E888/jwMHDuDEiRN49tln8dhjj+G+++6T6lKIetFqNT11OBymCgiXW/D9kKlh7J+k51vRmEs2BIxYYKyWRoZk08QB4LXXXsPy5csxe/ZsaLVa3HLLLXj66ad9f+/u7sbRo0fR3t4OACguLvbNsBo5cmSvc50+fRoZGRkICwvDunXr8NOf/hSCIGDkyJF46qmnsGTJEikvhaiX7HQrvjhej5LSZtx55TC5wwl6x2tb0OJwIsKgw5gkLvBHwPhUz5INdS0OVNo6MYRLNkhO7EVVyzCxpAlObGwsXn/99Qv+PSMjo1fmPWvWrItm4nPnzsXcuXP9FiPRpRBncezhhn8BIRY2ZqdboWfNE8GzZMPYlGgcqLBjT2kTExyJuc/pRVXLOlS8UxBdAnFF41N1bWhq65I5muDX0zWujhsrBYZvRiPrcCR3vLbV14sqrgWmdExwiC5BbKQBmQmRAHq6bUk6e1TWNU6BkXPO1ikkLfE+NylNPb2o6oiSSIFyvb0JYu8CSaOxrQun6tsAcAdx6k3c7FFcsoGk4+tFHWaVN5BBYIJDdImmZHgSnN1McCQl9t6MSIiENcIgczSkJMPjImE26eFwunGkqkXucIKa2gqMASY4RJdM/KLvLWtm61FCrL+hC9FqNcj2TRdnQ0MqTW1dOFXn7UVNV8/3kAkO0SXKjI+CNSIMDqcbByu5gatU1NhypMDJ8RYac6hYOmLymBkfiZhI9fSiMsEhukRarcbXq8CbqzScLjf2lnn2GmKCQ33xDRWf4XdQKr79p1T2HWSCQ3QZxB/dorONMkcSnI5Ut6Cj2wWzSY8RCVFyh0MKlDM0BloNUNHcgWpbp9zhBCW1DhMzwSG6DD0JThOXi5eAeGPNGRoDrVYjczSkRFFGPbKSzQCA3Wxo+J3T5cbe8mYA6utFZYJDdBkmpVmh12pQY3egvKlD7nCCTs8Gm+q6sVJgcZhKOkdrWtDe5UK0UY9RierqRWWCQ3QZwg06XJHqaT1ywT//E3tw1NZypMCakhELgLVwUij2fqbZQ62q60VlgkN0mXKHeW6ubD36V629E+VNHdBogEnpFrnDIQWb4k2AD1XZ0eZwyhxNcCn2boOhxl5UJjhEl+ncOhzyH7FHbExSNKJNYTJHQ0qWag1HqsUEl1vAXm7b4Fc9KxgzwSEKOeL4/5FqO1rZevQbNd9YKfByvcNUXFncf2pbOlHa2A6NpmdjUzVhgkN0mZLMJgyxhsMtcFdjf1Lr1FSShzhMxQTHf4rO9PSiWsLV14vKBIfID3r2peI0VX/o7HZhf4Vngb9p3pY5UX/EoeI9Z5vgcnPJBn/Y5U1wpqr0O8gEh8gPprAOx69KyprR7RKQGG1Eemy43OGQCmQlRyPSoEOLw4mj1dx40x92nfE02MQGnNowwSHyA7FOZE9pM1uPfrDrtOfGOnV4LDQadU1NJXnodVrkDOXK4v7S6nDiYKW3F3U4e3CIQlZWshmRBh1aHU4cq2Hr8XLt8vaETWWBMQ1CLutw/KaktBluARhiDUeKRZ29qExwiPxAp9X4Wo+8uV4el1vwLS42VaUtR5IHVzT2n0Lv8NRUlQ5PAUxwiPxGbD0WM8G5LIerPNPto8/ZY4hoILjxpv/sPtMzTKxWTHCI/ERMcMTCPLo04o118rAY6FS2NDzJixtv+ke3y4093iUv1DqDCmCCQ+Q3k4d5Wo/lTR2obObGm5eqZ2qqervGST4cprp8hyrt6Oh2wRIehpEJ6tpg81xMcIj8JMqox/ghnj2T2ItzaQRB8H12am45kny4dcrl23VO/Y3aNtg8FxMcIj8SF6XbeZoJzqUobWxHbYsDYToNJqlwaXiSn5gYc+PNS9ez/o26GxlMcIj8SCzI28UE55KIw1MT06wwhelkjobUKNUajiHWcLjcAntxLoEgCL7hPbUPEzPBIfIjsfV4vLYVDa0OmaNRHzExVOvKqaQMeZliT2qDzJGoz6n6NjS0dcGo1/qG3NWKCQ6RH8VGGjA6yVOUt4tFjoO2yzvzhftP0eXI8/akFrInddDEWYyT0q0w6tXdi8oEh8jPpvHmeknqWx04VdcGoKdQlOhS5A2PAwDsLbOhs9slczTqEkyzGJngEPnZNO/NtfAMu8cHQxz3H5MUDWuEQeZoSM2GxUUgyWxE1znrudDA7A6SAmOACQ6R34nDK4cq7bB3dsscjXqofediUg6NRuNraLAOZ+Bq7Z0409AOjSY4elElTXAaGxuxYMECmM1mWK1WLF68GK2trf2+ZtasWdBoNL0e99xzT69jSktLceONNyIiIgKJiYn42c9+BqeT0wFJGZItJgyLi4Bb4Focg8H1b8ifWIczeDu8n9W4FDPMpjCZo7l8eilPvmDBAlRVVWHTpk3o7u7GokWLsHTpUrz++uv9vm7JkiV4+OGHff8cERHh+/8ulws33ngjkpOT8dVXX6GqqgoLFy5EWFgYHnvsMcmuhWgwpmXE4mxDOwpPN+LaMYlyh6N49s5uHKiwAeiZAUN0OcQEp7i0CV1ONwx6DlhczI5Tnt6uKzPjZI7EPyT7N3748GFs3LgRzz//PPLy8jBz5kw888wzeOONN1BZWdnvayMiIpCcnOx7mM09G+59+umnOHToEF599VVkZ2fj+uuvxyOPPIJ169ahq6tLqsshGpSpbD0Oyu4zjXALQEZcBFIs4XKHQ0FgZGIUYiMN6Ox2Y39Fs9zhqAITnAHavn07rFYrpkyZ4nsuPz8fWq0WO3fu7Pe1r732GuLj4zF+/HisWrUK7e3tvc47YcIEJCUl+Z6bM2cO7HY7Dh482Of5HA4H7HZ7rweRlMTW477yZnR0cRbHxew45UkEg+XGSvLTaDS+ejjxvy+6sNqWTpyqa4NGEzzLNEiW4FRXVyMxsXfXvF6vR2xsLKqrqy/4uh/84Ad49dVX8fnnn2PVqlX4+9//jjvuuKPXec9NbgD4/vlC5127di0sFovvkZ6efqmXRTQgQ2M9szi6XQL2lLEO52KCreVIytCz4B8TnIvZ6U0CxyabYYlQf/0NcAkJzkMPPXReEfDXH0eOHLnkgJYuXYo5c+ZgwoQJWLBgAV555RW89957OHny5CWfc9WqVbDZbL5HWVnZJZ+LaCDOncXBYar+sf6GpCKuh1N0phFOl1vmaJQtGBsZgy4yvv/++3HXXXf1e0xmZiaSk5NRW1vb63mn04nGxkYkJycP+P3y8vIAACdOnMCIESOQnJyMwsLCXsfU1NQAwAXPazQaYTQaB/yeRP4wbXgsPtxbyZ3FL4L1NySVMcnRMJv0sHc6cbDSzg1c+9GT4ARPI2PQCU5CQgISEhIuetz06dPR3NyMoqIi5ObmAgA+++wzuN1uX9IyECUlJQCAlJQU33l/85vfoLa21jcEtmnTJpjNZowbN26QV0MkHbEOp+hsExxOl+qXPZcK629IKjqtBtOGx+I/h2tReLqRCc4F1LZ04qRYfzM8eBIcyWpwxo4di7lz52LJkiUoLCzEtm3bsHz5csyfPx+pqakAgIqKCmRlZfl6ZE6ePIlHHnkERUVFOHPmDD744AMsXLgQV199NSZOnAgAuO666zBu3Djceeed2Lt3Lz755BP84he/wL333steGlKUUYlRiPPO4ijhaqoXFIxd46Qc4g82F/y7sHPrb4JpFXFJFwZ47bXXkJWVhdmzZ+OGG27AzJkz8de//tX39+7ubhw9etQ3S8pgMOA///kPrrvuOmRlZeH+++/HLbfcgg8//ND3Gp1Ohw0bNkCn02H69Om44447sHDhwl7r5hApgUajwfQRnh/tr07y5toX1t+Q1PLOqYVzuQWZo1EmsZERbN9BSRf6i42N7XdRv4yMDAhCz39w6enp2LJly0XPO2zYMHz88cd+iZFISjNGxGPDvipsP9mAn35T7miUh/U3JLUrUs2IMnrqcA5X2TF+iEXukBRHnGUWbL2oXNqRSEIzvD04e8qa0N7F7US+bvtJDk+RtPQ6ra8ebtuJepmjUZ66FgdO1LZCo+mpGwwWTHCIJDQsLgKpFhO6XYJvt2zqwQJjCgRxqHgbh4rPI9YmZQVZ/Q3ABIdIUp46nHgArMP5OltHNw5Wsv6GpHfVSM93cNfpRnQ5uR7OuYJxeriICQ6RxK4a6Wk9bj/J7vFzsf6GAmVMUjTiIg3o6HZhTyl7Us/1VRAPEzPBIZKY2D2+v8IGe2e3zNEox7YTnhur+PkQSUWr5YzGvlQ2d+BUXRu0GiY4RHQJUizhyIyPhFsACrnpn49Y8Dlz5MUXDiW6XOIw1VfsSfURv4MT06ywhAfH/lPnYoJDFABsPfZWa+/E0ZoWaDTswaHAuMpbC7entBltDs5oBM5tZMTLHIk0mOAQBcCMEWw9nmub93O4ItWM2MjgmrlByjQ0LgJpMeFwugUUcn84CIKAL73DxFcxwSGiSyXOUDhS3YKGVofM0cjvy+PBfWMlZRJ7cb7iejg4VtOK+lYHTGFaTB5mlTscSTDBIQqAuCgjspKjAfSs/RKqPC3HOgDAN1h/QwE0wzujUSxwD2VfepO8acPjgnYjYCY4RAEiDlOJP+6h6mRdK2rsDhj0WkzJiJE7HAoh4nfwUJUdjW1dMkcjr576m+CtgWOCQxQg3xjtubluPVbfaw+2UPPlcc+NdWpGDExhwdlyJGVKiDZiTJKnJ3V7CBf8d7vc2Hkq+IeJmeAQBUje8FgYdFpUNHfgVH2b3OHIRuwaD+YbKymXOEz1ZQjX4ewta0ZblwuxkQaMTTbLHY5kmOAQBUiEQY+pwz1DMluPheYwVbfL7atBYv0NyUGcEv3F8bqQ7UkVk7vpI+Kg1WpkjkY6THCIAujqUZ4f9VBNcPaVN6PV4YQ1IgzjUoO35UjKdWVmHAw6LcqbOnA6RHtSg339GxETHKIAunq0J8HZcaoRDqdL5mgC7wtv/c2MEXHQBXHLkZQr0tjTk7olBBsato5uFJc2A2CCQ0R+lJUcjYRoIzq6Xdh9JvQ2/dt81Ds9fBSHp0g+13gbGqGY4Gw7UQ+XW8CIhEikx0bIHY6kmOAQBZBGo8E3RomzqULr5trY1oW95c0AgFljmOCQfK4ZnQgA2HGqAZ3dodWTuvloLQBg1phEmSORHhMcogAL1dajp6jT04uVYgmXOxwKYaOTopBsNqGz243C06Gz8KYgCL77Tig0MpjgEAXYzJHx0Gg82zbU2jvlDidgPj8SOi1HUjaNRhOSDY3DVS2osTsQHqbDtOGxcocjOSY4RAEWF2XE+FQLAGDr8dBYi8PtFnzXGgotR1K+a8aEXoKz+ZinkTFjRPBuz3AuJjhEMrh6dGjV4eyrsKGxrQvRRj1yh3F7BpLfVSPjodNqcKK2FeVN7XKHExBikX+oNDKY4BDJQBym2XKsDk6XW+ZopCcOT80cFY8wHW87JD9LeBhy0q0APNunBDt7ZzeKznpmbobKMDHvNEQyyEm3whoRBltHz00nmG0OocJGUg+xDkecWRTMth33TA/PDIHp4SImOEQy0Ou0uNbbiio4Etw314ZWB/b5poeHRsuR1EGsw9l2oj7oF970DU+NDp3vIBMcIpnMHuu50fzncI3MkUhrq3d6+NgUM5LMJrnDIfIZn2pBktmIti6Xb4+0YBRq08NFTHCIZHL16ATotRqcqmsL6j1xxJbjtSF0YyV10Go1+K+sJADAfw4Fb0PjYKUd1fZORBhCY3q4iAkOkUzMpjDfzaYgSHtxul1uX4Hxf2WFTtc4qcc3x3mHig/XBO3u4p96k7erRyXAFBb808NFTHCIZDR7rKf1WHA4OOtwCk83wt7pRHyUATlDOT2clGfGiHiYwrSotHXiUJVd7nAkscmb4HxzXJLMkQQWExwiGeV763B2nWmEraNb5mj879OD1QCA2VlJ3D2cFMkUpvNt/vqfQ8HX0ChrbMfhKju0GuDaEOtFZYJDJKNhcZEYmRgFp1sIukX/BEEI2ZYjqcs3vT2pwVjwL17TlIxYxEYaZI4msCRNcBobG7FgwQKYzWZYrVYsXrwYra2tFzz+zJkz0Gg0fT7efvtt33F9/f2NN96Q8lKIJDM7q6cGIJgcrLSj0taJ8DAdZnp3UCdSomuzEqHRAPsrbKi2Bdf+cGIj47oQbGRImuAsWLAABw8exKZNm7BhwwZs3boVS5cuveDx6enpqKqq6vX49a9/jaioKFx//fW9jv3b3/7W67h58+ZJeSlEkhHrcD47UosuZ/CsauwrbBwdH1KFjaQ+CdFGZHtXNQ6mXhxbezd2endLD8VeVMkSnMOHD2Pjxo14/vnnkZeXh5kzZ+KZZ57BG2+8gcrKyj5fo9PpkJyc3Ovx3nvv4bbbbkNUVFSvY61Wa6/jTCaur0HqlDssBvFRBtg7ndh+qkHucPymp+WYLHMkRBcnJgCfeOvGgsHnR2vhcgsYnRSFYXGRcocTcJIlONu3b4fVasWUKVN8z+Xn50Or1WLnzp0DOkdRURFKSkqwePHi8/527733Ij4+HtOmTcOLL77Y7/Q+h8MBu93e60GkFDqtBnOu8CQBGw9UyRyNf4iFjTqthtPDSRWuH58CAPjqZAOa2rpkjsY/Qr0GTrIEp7q6GomJvW9ser0esbGxqK4eWIb8wgsvYOzYsZgxY0av5x9++GG89dZb2LRpE2655Rb86Ec/wjPPPHPB86xduxYWi8X3SE9PH/wFEUlIvLl+crAmKDbfFFvBU4bFICbEChtJnYbHR2Jsihkud09xvJp1dLnwmXcNKrEBFWoGneA89NBDFywEFh9Hjhy57MA6Ojrw+uuv99l788tf/hJXXXUVcnJy8OCDD+KBBx7AE088ccFzrVq1CjabzfcoKyu77PiI/CkvMxYxEWFobOtC4Wn1Lxn/0X5PT9QNE1JkjoRo4G4Y70kEPg6CntTPj9aio9uF9NhwTBhikTscWQw6wbn//vtx+PDhfh+ZmZlITk5GbW3vNQWcTicaGxuRnHzxbPKf//wn2tvbsXDhwosem5eXh/Lycjgcjj7/bjQaYTabez2IlCRMp/XVqvz7gLprAMqb2rGntBkaDXD9hNBsOZI63TDRk5BvO1EPW7u616X6aJ8nSbtxQio0mtBcg0o/2BckJCQgIeHie8pMnz4dzc3NKCoqQm5uLgDgs88+g9vtRl5e3kVf/8ILL+Cmm24a0HuVlJQgJiYGRqPx4hdApFBzJyTjzd1l2HiwGr+66QrVLoz3sbf3Jm94LBKjWfxP6jEiIQpjkqJxtKYFmw7X4Hu5aXKHdEnau5woOOIZZvvWxNDtRZWsBmfs2LGYO3culixZgsLCQmzbtg3Lly/H/PnzkZqaCgCoqKhAVlYWCgsLe732xIkT2Lp1K+6+++7zzvvhhx/i+eefx4EDB3DixAk8++yzeOyxx3DfffdJdSlEAXHViHhEm/Soa3Gg6GyT3OFcsg3eluO3JqbKHAnR4Im9jv/er95hqs+O1KKz241hcRG4IjV0RywkXQfntddeQ1ZWFmbPno0bbrgBM2fOxF//+lff37u7u3H06FG0t7f3et2LL76ItLQ0XHfddeedMywsDOvWrcP06dORnZ2Nv/zlL3jqqaewZs0aKS+FSHIGvdY32+GjfX0vpaB0pQ3t2Fdug1YDXD+ew1OkPjd668a+OF4Pe6c6h6nE4akbJqSE7PAUAGiEYN0+tR92ux0WiwU2m431OKQonx+pxaKXdiEu0oAdP5+NMJ26dlP58+YTeHzjUcwcGY9X7774UDSREuU/tQUnalvxxPcm4tYp6pp12+ZwYvIjm+BwurHhvpkYH2QFxoP5/VbX3ZMoyM0cFY+4SAMa2rrw5Yl6ucMZtA17xeGp0B33J/Wbl+0ZXn1vT4XMkQxewZFaOJxuZIT48BTABIdIUcJ0Wnx7kufm+r7Kbq4naltxqMoO/TkLFxKp0c3ZQwAA2081oMrWIXM0gyPeN741MXRnT4mY4BApzLwcz831k4PVaHU4ZY5m4N4tLgcAXDM6gYv7kaqlx0ZgWkYsBAH4V4l66uHqWhzYcqwOAPDdyUNkjkZ+THCIFGZSmgXD4yPR2e3GpyrZF8flFnzd+beodGot0bm+400Q3iuu6HcrICX5YG8lXG4BOUOtyEyIuvgLghwTHCKF0Wg0mOftIldLDcD2kw2osnXCEh6G2WO59xSp3w0TUmDQa3G0pgWHqtSxf6HYi/rdyWxkAExwiBRpXo6nDmfbiXrU2jtljubi3vHeWL89KQVGvU7maIgunyU8DPneZF0N9XBHqu04WGlHmE6Db7PIHwATHCJFGhYXidxhMXALwDvFyr65tjqc2OjdXoItRwomYk/q+yWVit8E913vfWJ2VhKsEayBA5jgECnW7VM962+8sasUbrdyawA+3leFjm4XMuMjkZNulTscIr+ZNSYR8VEG1LU4UHCk9uIvkEmX0413ijy9qKyB68EEh0ihvjUxBdFGPc42tGP7qQa5w7mg13aeBQDcOiU95KelUnAx6LX4Xq6nofH6zlKZo7mwTw5Wo6GtC8lmE64dc/H9G0MFExwihYow6H1Txl8vVObNdX+5DXvLbTDotLhtCluOFHy+P82T4Gw9XoeyxvaLHC0PsZFx29R06FW2+rmU+EkQKdj3pw0FAHx6sBr1rQ6ZozmfeGO9fkIy4qKMMkdD5H/D4iIxc2Q8BMEzXKw0J+taseNUI7QaYP5UdW0rITUmOEQKNi7VjElpFnS7BLy9u1zucHqxd3b7FkFbkDdM5miIpPODPE9D463d5ehWWLHxP7xDZ/+VlYhUa7jM0SgLExwihVtwpSd5+Pv2M4qayfFecQU6ul0YlRiFqRkxcodDJJlvjktCfJQRdS0O/PuAchbfbO9y4m1vcbGYhFEPJjhECnfTpFTERxlQaetUzM3V5Rbw0ldnAAAL8oayuJiCWphOizuu9CQQL3xxSjErG79TVA5bRzeGxkbgmtFcYPPrmOAQKZwpTOcbAnrhy9MyR+Pxn8M1OF3fBrNJj1uncNyfgt+dVw6DQa/F3nIbdp9tkjscuNyC737ww6syoNOykfF1THCIVOCOK4fBoNOipKwZRQq4uf516ykAnrgijXqZoyGSXlyUEbd496d6/otTMkfjaWScaWhnI6MfTHCIVCAh2oibsz3bN8h9cy0624iis00w6LS4a0aGrLEQBdLimcMBAJ8eqsGZ+jZZY3nO28hYwEbGBTHBIVKJu7+RCQDYeLAaR6tbZItD7L2Zl5OKRLNJtjiIAm1kYjSuHZMAQQDWbzkpWxxFZxux+2wTwnQaNjL6wQSHSCXGJEfj+vHJEATg6c+OyxLDoUo7PjlYA40GWOJNuIhCyfL/GgkA+GdROUob5Fn47w//8Xz/v5uThiQ2Mi6ICQ6Rivx49igAwMf7q3CsJvC9OH/4zzEAwI0TUjAqKTrg708kt9xhsbh6dAKcbgHPyNDQ2H2mEV8cr4deq/ElW9Q3JjhEKjI2xdzTi1MQ2Jvr/nIbPj1UA60GWJE/OqDvTaQkP833NDTe3VMR8Fqc//M2Mm6dkob02IiAvrfaMMEhUhmxF+ej/VU4UGELyHsKgoDHPzkCALg5ewhGJkYF5H2JlChnaAyuHZMAl1vwJRyBsOVYHbadaECYToMfzWLvzcUwwSFSmbEpZszLToUgAA9vOBSQRcc+P1qLL47Xw6DTYoW39UoUyu6/bgwA4F8llSgulX7pBqfLjd98dAgAsHB6BntvBoAJDpEKPTA3C6YwLQpPN2KjxKsbdzndeHTDYQDAopkZGBYXKen7EanB+CEW3JqbBgD49YeH4HZL29B4c3cZjtW0whoRhh//FxsZA8EEh0iFUq3hWHr1CADAox8dRpvDKdl7/W3baZyqb0N8lAHLr2W3OJHoZ3PHINKgw96yZrxTLN1muPWtDjzxyVEAwIrZo2CJCJPsvYIJExwilbrnmkykxYSjornDd/Pzt1N1rXhqk6fG4IG5WYg28cZKJEqMNvlq4h796DBq7Z2SvM+vPzyE5vZujEsx+zbfpYtjgkOkUhEGPdZ+dwIA4OXtZ7DrTKNfz+92C3jonf1wON34xqh4X3c8EfVYPHM4JgyxwNbRjV+8f8DvNXH/OVSDD/dWQqsBfnfLRITp+LM9UPykiFTsG6MScGtuGgQBWPFGCZrbu/x27me3nEThmUZEGHR47DsTuGM4UR/0Oi2euHUiwnQafHqoBm/tLvPbuatsHXjgnX0APAtrTkiz+O3coYAJDpHK/fLb4zA0NgIVzR24/629fil2/OpkPX7/qWfYa823x3HGBlE/spLN+Ok3PWtD/fJfB/2yfIPT5caP/7EHjW1duCK15/w0cExwiFTObArDnxdMhkGvRcGRWl/NzKU6U9+G+17fA7cAfC83Dbdxp2Kii7rn6hHIH5uILqcb97xahNqWS6/HEQQBv/zXQew604Roox5/XjAZpjCdH6MNDUxwiILA+CEWPHrzeADAnz4/gRe/PH1J56m2dWLB8zvR0NaFcSlmPHLzeA5NEQ2AVqvB72/LxrC4CJQ3deDO5wsvecj4/zYdwz8KS6HVAL+/bRKXZrhEkiU4v/nNbzBjxgxERETAarUO6DWCIGD16tVISUlBeHg48vPzcfx47+XoGxsbsWDBApjNZlitVixevBitra0SXAGRutw2NR3/c52nG/vhDYfw160nB1XwWNrQjh88twMVzR3IiIvAyz+chnADW41EA2UJD8MrP5yGxGgjjta0YMHzOwc1s0oQBDy+8Qie/uwEAODReRNw3RXJUoUb9CRLcLq6unDrrbdi2bJlA37N448/jqeffhrr16/Hzp07ERkZiTlz5qCzs+c/kAULFuDgwYPYtGkTNmzYgK1bt2Lp0qVSXAKR6tx77Uj899WeXb4f+/gIHvjnvgGtkfP50Vp858/bcKq+DUOs4fj74jwkRBulDpco6AyLi8Rrd+chNtKAg5V2zFu3DYWnLz7Dsbm9C8teLcafN58EADx0fRZ+kDdU6nCDmkaQeJ33l156CStWrEBzc3O/xwmCgNTUVNx///34n//5HwCAzWZDUlISXnrpJcyfPx+HDx/GuHHjsGvXLkyZMgUAsHHjRtxwww0oLy9HamrqgGKy2+2wWCyw2Wwwm82XdX1ESiMIAp7/4jQe+/dhCAIwxBqOFfmj8O1JqeeN4x+racEzn53Ah3srAQBXpJrxt7umItFskiN0oqBxtqENi17ahVN1bdBogNty0/Gja0ecN9zU5nDi3T0V+ON/jqO+1YEwnQaPzhuP26cyuenLYH6/9QGK6aJOnz6N6upq5Ofn+56zWCzIy8vD9u3bMX/+fGzfvh1Wq9WX3ABAfn4+tFotdu7cie985zt9ntvhcMDhcPj+2W63S3chRDLTaDRYcnUmxg+x4H/e3ouK5g787J/78PCHh5CbEYMUSzg6u104WGnDsZqe4d1FV2XgwblZLGYk8oNhcZF4/96r8OiGQ3hrdzne3F2GN3eXYVyKGVkp0TDqtShv6kDR2Sa0d7kAAJkJkfjj7TmcDu4niklwqqs9++kkJSX1ej4pKcn3t+rqaiQmJvb6u16vR2xsrO+Yvqxduxa//vWv/RwxkbJNHxGH/6y8Bi99dQZ/334GlbZObD5a1+sYvVaD2WMT8ePZo3BFKm+qRP5kNoXh8e9Nwu1T0/HHghP48ngdDlXZcaiqdyM7Iy4Cd83IwA/yhsGg59wffxlUgvPQQw/hd7/7Xb/HHD58GFlZWZcVlL+tWrUKK1eu9P2z3W5HejqnvlLwCzfosGzWCPz31ZnYW96MA5V2NLV1Qa/TIDM+CnnDYxETaZA7TKKgljssFq/8cBrqWhwoPN2IMw1tcLsFxEUZMTHNgitSzZytKIFBJTj3338/7rrrrn6PyczMvKRAkpM9leI1NTVISUnxPV9TU4Ps7GzfMbW1tb1e53Q60djY6Ht9X4xGI4xGFkxS6NJqNcgZGoOcoTFyh0IUshKijbhxYsrFDyS/GFSCk5CQgISEBEkCGT58OJKTk1FQUOBLaOx2O3bu3OmbiTV9+nQ0NzejqKgIubm5AIDPPvsMbrcbeXl5ksRFRERE6iPZYF9paSlKSkpQWloKl8uFkpISlJSU9FqzJisrC++99x4AT2HkihUr8Oijj+KDDz7A/v37sXDhQqSmpmLevHkAgLFjx2Lu3LlYsmQJCgsLsW3bNixfvhzz588f8AwqIiIiCn6SFRmvXr0aL7/8su+fc3JyAACff/45Zs2aBQA4evQobLaePTseeOABtLW1YenSpWhubsbMmTOxceNGmEw9U1Zfe+01LF++HLNnz4ZWq8Utt9yCp59+WqrLICIiIhWSfB0cJeI6OEREROozmN9vzkcjIiKioMMEh4iIiIIOExwiIiIKOkxwiIiIKOgwwSEiIqKgwwSHiIiIgg4THCIiIgo6THCIiIgo6DDBISIioqAj2VYNSiYu3my322WOhIiIiAZK/N0eyCYMIZngtLS0AADS09NljoSIiIgGq6WlBRaLpd9jQnIvKrfbjcrKSkRHR0Oj0cgSg91uR3p6OsrKyrgfVh/4+VwYP5v+8fPpHz+f/vHzuTAlfDaCIKClpQWpqanQavuvsgnJHhytVou0tDS5wwAAmM1mfon6wc/nwvjZ9I+fT//4+fSPn8+Fyf3ZXKznRsQiYyIiIgo6THCIiIgo6DDBkYnRaMSaNWtgNBrlDkWR+PlcGD+b/vHz6R8/n/7x87kwtX02IVlkTERERMGNPThEREQUdJjgEBERUdBhgkNERERBhwkOERERBR0mOArx0UcfIS8vD+Hh4YiJicG8efPkDklxHA4HsrOzodFoUFJSInc4inDmzBksXrwYw4cPR3h4OEaMGIE1a9agq6tL7tBks27dOmRkZMBkMiEvLw+FhYVyhyS7tWvXYurUqYiOjkZiYiLmzZuHo0ePyh2WYv32t7+FRqPBihUr5A5FMSoqKnDHHXcgLi4O4eHhmDBhAnbv3i13WP1igqMA77zzDu68804sWrQIe/fuxbZt2/CDH/xA7rAU54EHHkBqaqrcYSjKkSNH4Ha78Ze//AUHDx7E//3f/2H9+vX4+c9/LndosnjzzTexcuVKrFmzBsXFxZg0aRLmzJmD2tpauUOT1ZYtW3Dvvfdix44d2LRpE7q7u3Hdddehra1N7tAUZ9euXfjLX/6CiRMnyh2KYjQ1NeGqq65CWFgY/v3vf+PQoUP4/e9/j5iYGLlD659Asuru7haGDBkiPP/883KHomgff/yxkJWVJRw8eFAAIOzZs0fukBTr8ccfF4YPHy53GLKYNm2acO+99/r+2eVyCampqcLatWtljEp5amtrBQDCli1b5A5FUVpaWoRRo0YJmzZtEq655hrhJz/5idwhKcKDDz4ozJw5U+4wBo09ODIrLi5GRUUFtFotcnJykJKSguuvvx4HDhyQOzTFqKmpwZIlS/D3v/8dERERcoejeDabDbGxsXKHEXBdXV0oKipCfn6+7zmtVov8/Hxs375dxsiUx2azAUBI/nfSn3vvvRc33nhjr/+GCPjggw8wZcoU3HrrrUhMTEROTg6ee+45ucO6KCY4Mjt16hQA4Fe/+hV+8YtfYMOGDYiJicGsWbPQ2Ngoc3TyEwQBd911F+655x5MmTJF7nAU78SJE3jmmWfw3//933KHEnD19fVwuVxISkrq9XxSUhKqq6tlikp53G43VqxYgauuugrjx4+XOxzFeOONN1BcXIy1a9fKHYrinDp1Cs8++yxGjRqFTz75BMuWLcOPf/xjvPzyy3KH1i8mOBJ56KGHoNFo+n2I9RMA8L//+7+45ZZbkJubi7/97W/QaDR4++23Zb4K6Qz083nmmWfQ0tKCVatWyR1yQA308zlXRUUF5s6di1tvvRVLliyRKXJSunvvvRcHDhzAG2+8IXcoilFWVoaf/OQneO2112AymeQOR3HcbjcmT56Mxx57DDk5OVi6dCmWLFmC9evXyx1av/RyBxCs7r//ftx11139HpOZmYmqqioAwLhx43zPG41GZGZmorS0VMoQZTXQz+ezzz7D9u3bz9v7ZMqUKViwYIHiWxCXaqCfj6iyshLXXnstZsyYgb/+9a8SR6dM8fHx0Ol0qKmp6fV8TU0NkpOTZYpKWZYvX44NGzZg69atSEtLkzscxSgqKkJtbS0mT57se87lcmHr1q3405/+BIfDAZ1OJ2OE8kpJSen1GwUAY8eOxTvvvCNTRAPDBEciCQkJSEhIuOhxubm5MBqNOHr0KGbOnAkA6O7uxpkzZzBs2DCpw5TNQD+fp59+Go8++qjvnysrKzFnzhy8+eabyMvLkzJEWQ308wE8PTfXXnutr/dPqw3NjlmDwYDc3FwUFBT4lllwu90oKCjA8uXL5Q1OZoIg4L777sN7772HzZs3Y/jw4XKHpCizZ8/G/v37ez23aNEiZGVl4cEHHwzp5AYArrrqqvOWFTh27Jjif6OY4MjMbDbjnnvuwZo1a5Ceno5hw4bhiSeeAADceuutMkcnv6FDh/b656ioKADAiBEj2AKFJ7mZNWsWhg0bhieffBJ1dXW+v4Vir8XKlSvx//7f/8OUKVMwbdo0/OEPf0BbWxsWLVokd2iyuvfee/H666/jX//6F6Kjo301SRaLBeHh4TJHJ7/o6Ojz6pEiIyMRFxfHOiUAP/3pTzFjxgw89thjuO2221BYWIi//vWviu8tZoKjAE888QT0ej3uvPNOdHR0IC8vD5999pny1xgg2W3atAknTpzAiRMnzkv4BEGQKSr53H777airq8Pq1atRXV2N7OxsbNy48bzC41Dz7LPPAgBmzZrV6/m//e1vFx0KJZo6dSree+89rFq1Cg8//DCGDx+OP/zhD1iwYIHcofVLI4TiXZCIiIiCWmgO1hMREVFQY4JDREREQYcJDhEREQUdJjhEREQUdJjgEBERUdBhgkNERERBhwkOERERBR0mOERERBR0mOAQERFR0GGCQ0REREGHCQ4REREFHSY4REREFHT+P1mI5s9mrQiYAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a3ceebfc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c5060938",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 输入\n",
    "x_tran_input = x_train.unsqueeze(1)\n",
    "x_tran_input.requires_grad_(True)\n",
    "x_tran_input.requires_grad\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8099441b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "        [-1.9991e-01],\n",
       "        [-1.8757e-01],\n",
       "        [-1.7520e-01],\n",
       "        [-1.6280e-01],\n",
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       "        [-1.3793e-01],\n",
       "        [-1.2546e-01],\n",
       "        [-1.1297e-01],\n",
       "        [-1.0046e-01],\n",
       "        [-8.7939e-02],\n",
       "        [-7.5402e-02],\n",
       "        [-6.2853e-02],\n",
       "        [-5.0294e-02],\n",
       "        [-3.7728e-02],\n",
       "        [-2.5155e-02],\n",
       "        [-1.2578e-02],\n",
       "        [ 1.7485e-07]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 输出结果\n",
    "y_train_target = y_train.unsqueeze(1)\n",
    "y_train_target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d4f6e4e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "16cf9b61",
   "metadata": {},
   "source": [
    "# 模拟 SIN(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5dcf9006",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "        [-1.2578e-02],\n",
       "        [ 1.7485e-07]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 原始数据\n",
    "# x_train = torch.linspace(-1, 1, 100)\n",
    "# y_train = target_function(x_train)\n",
    "\n",
    "x_train = torch.linspace(-2*torch.pi, 2*torch.pi, 1000)\n",
    "y_train = target_function(x_train)\n",
    "\n",
    "# 可训练数据\n",
    "x_tran_input = x_train.unsqueeze(1)\n",
    "x_tran_input.requires_grad_(True)\n",
    "x_tran_input.requires_grad\n",
    "\n",
    "y_train_target = y_train.unsqueeze(1)\n",
    "y_train_target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0ff4721c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5234\n",
      "0.0008\n",
      "0.0001\n",
      "0.0001\n",
      "0.0000\n",
      "0.0006\n",
      "0.0001\n",
      "0.0005\n"
     ]
    }
   ],
   "source": [
    "# 构造模型\n",
    "class Model(nn.Module):\n",
    "    def __init__(self, *args, **kwargs):\n",
    "        super().__init__(*args, **kwargs)\n",
    "        self.hidden1 = nn.Linear(in_features=1,out_features=64)\n",
    "        self.hidden2 = nn.Linear(in_features=64,out_features=64)\n",
    "        self.hidden3 = nn.Linear(in_features=64,out_features=64)\n",
    "        self.hidden4 = nn.Linear(in_features=64,out_features=64)\n",
    "        self.hidden5 = nn.Linear(in_features=64,out_features=64)\n",
    "        self.fc = nn.Linear(in_features=64,out_features=1)\n",
    "    \n",
    "    def forward(self,input):\n",
    "        # (1000,1)的大小\n",
    "        x = nn.ReLU()(self.hidden1(input))\n",
    "        x = nn.ReLU()(self.hidden2(x))\n",
    "        x = nn.ReLU()(self.hidden3(x))\n",
    "        x = nn.ReLU()(self.hidden4(x))\n",
    "        x = nn.ReLU()(self.hidden5(x))\n",
    "        x = self.fc(x)\n",
    "        return x\n",
    "\n",
    "\n",
    "model = Model()\n",
    "\n",
    "\n",
    "opt = torch.optim.Adam(model.parameters(),lr=1e-3)\n",
    "cirtical = nn.MSELoss()\n",
    "epoch = 8000\n",
    "for r in range(epoch):\n",
    "    \n",
    "    opt.zero_grad()\n",
    "    predict = model(x_tran_input)\n",
    "    loss = cirtical(predict,y_train_target)\n",
    "    \n",
    "    loss.backward()\n",
    "    opt.step()\n",
    "    if r%1000==0:\n",
    "        print(\"{:.4f}\".format(loss.item()))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9d2418fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "        [-0.0119]])\n"
     ]
    }
   ],
   "source": [
    "# 预测结果\n",
    "with torch.no_grad():\n",
    "    res = model(x_tran_input)\n",
    "    print(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4467787b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2d45f7f1ca0>]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画图 预测值和真实值\n",
    "plt.plot(x_train,res)\n",
    "plt.plot(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5099e69f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c31969de",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "e834b59f",
   "metadata": {},
   "source": [
    "# sigmoid函数 模拟"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3338e0f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7494\n",
      "0.2429\n",
      "0.0158\n",
      "0.0000\n",
      "0.0000\n",
      "0.0000\n",
      "0.0000\n",
      "0.0000\n"
     ]
    }
   ],
   "source": [
    "# 构造模型\n",
    "class Model(nn.Module):\n",
    "    def __init__(self, *args, **kwargs):\n",
    "        super().__init__(*args, **kwargs)\n",
    "        self.hidden1 = nn.Linear(in_features=1,out_features=64)\n",
    "        self.hidden2 = nn.Linear(in_features=64,out_features=64)\n",
    "        self.hidden3 = nn.Linear(in_features=64,out_features=64)\n",
    "        self.hidden4 = nn.Linear(in_features=64,out_features=64)\n",
    "        self.hidden5 = nn.Linear(in_features=64,out_features=64)\n",
    "        self.fc = nn.Linear(in_features=64,out_features=1)\n",
    "    \n",
    "    def forward(self,input):\n",
    "        # (1000,1)的大小\n",
    "        x = nn.Sigmoid()(self.hidden1(input))\n",
    "        x = nn.Sigmoid()(self.hidden2(x))\n",
    "        x = nn.Sigmoid()(self.hidden3(x))\n",
    "        x = nn.Sigmoid()(self.hidden4(x))\n",
    "        x = nn.Sigmoid()(self.hidden5(x))\n",
    "        x = self.fc(x)\n",
    "        return x\n",
    "\n",
    "\n",
    "model = Model()\n",
    "\n",
    "\n",
    "opt = torch.optim.Adam(model.parameters(),lr=1e-3)\n",
    "cirtical = nn.MSELoss()\n",
    "epoch = 8000\n",
    "for r in range(epoch):\n",
    "    \n",
    "    opt.zero_grad()\n",
    "    predict = model(x_tran_input)\n",
    "    loss = cirtical(predict,y_train_target)\n",
    "    \n",
    "    loss.backward()\n",
    "    opt.step()\n",
    "    if r%1000==0:\n",
    "        print(\"{:.4f}\".format(loss.item()))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d514e3cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[ 1.7032e-03],\n",
      "        [ 1.3497e-02],\n",
      "        [ 2.5373e-02],\n",
      "        [ 3.7324e-02],\n",
      "        [ 4.9346e-02],\n",
      "        [ 6.1431e-02],\n",
      "        [ 7.3575e-02],\n",
      "        [ 8.5770e-02],\n",
      "        [ 9.8012e-02],\n",
      "        [ 1.1029e-01],\n",
      "        [ 1.2261e-01],\n",
      "        [ 1.3495e-01],\n",
      "        [ 1.4731e-01],\n",
      "        [ 1.5969e-01],\n",
      "        [ 1.7208e-01],\n",
      "        [ 1.8448e-01],\n",
      "        [ 1.9687e-01],\n",
      "        [ 2.0925e-01],\n",
      "        [ 2.2162e-01],\n",
      "        [ 2.3397e-01],\n",
      "        [ 2.4630e-01],\n",
      "        [ 2.5860e-01],\n",
      "        [ 2.7087e-01],\n",
      "        [ 2.8310e-01],\n",
      "        [ 2.9529e-01],\n",
      "        [ 3.0743e-01],\n",
      "        [ 3.1952e-01],\n",
      "        [ 3.3155e-01],\n",
      "        [ 3.4353e-01],\n",
      "        [ 3.5544e-01],\n",
      "        [ 3.6729e-01],\n",
      "        [ 3.7907e-01],\n",
      "        [ 3.9078e-01],\n",
      "        [ 4.0242e-01],\n",
      "        [ 4.1397e-01],\n",
      "        [ 4.2546e-01],\n",
      "        [ 4.3686e-01],\n",
      "        [ 4.4817e-01],\n",
      "        [ 4.5941e-01],\n",
      "        [ 4.7055e-01],\n",
      "        [ 4.8161e-01],\n",
      "        [ 4.9258e-01],\n",
      "        [ 5.0346e-01],\n",
      "        [ 5.1425e-01],\n",
      "        [ 5.2495e-01],\n",
      "        [ 5.3555e-01],\n",
      "        [ 5.4606e-01],\n",
      "        [ 5.5648e-01],\n",
      "        [ 5.6680e-01],\n",
      "        [ 5.7703e-01],\n",
      "        [ 5.8716e-01],\n",
      "        [ 5.9720e-01],\n",
      "        [ 6.0713e-01],\n",
      "        [ 6.1698e-01],\n",
      "        [ 6.2672e-01],\n",
      "        [ 6.3636e-01],\n",
      "        [ 6.4591e-01],\n",
      "        [ 6.5536e-01],\n",
      "        [ 6.6471e-01],\n",
      "        [ 6.7395e-01],\n",
      "        [ 6.8310e-01],\n",
      "        [ 6.9214e-01],\n",
      "        [ 7.0109e-01],\n",
      "        [ 7.0993e-01],\n",
      "        [ 7.1866e-01],\n",
      "        [ 7.2729e-01],\n",
      "        [ 7.3581e-01],\n",
      "        [ 7.4423e-01],\n",
      "        [ 7.5253e-01],\n",
      "        [ 7.6073e-01],\n",
      "        [ 7.6881e-01],\n",
      "        [ 7.7678e-01],\n",
      "        [ 7.8463e-01],\n",
      "        [ 7.9237e-01],\n",
      "        [ 7.9999e-01],\n",
      "        [ 8.0749e-01],\n",
      "        [ 8.1487e-01],\n",
      "        [ 8.2213e-01],\n",
      "        [ 8.2926e-01],\n",
      "        [ 8.3626e-01],\n",
      "        [ 8.4313e-01],\n",
      "        [ 8.4987e-01],\n",
      "        [ 8.5648e-01],\n",
      "        [ 8.6295e-01],\n",
      "        [ 8.6929e-01],\n",
      "        [ 8.7549e-01],\n",
      "        [ 8.8155e-01],\n",
      "        [ 8.8747e-01],\n",
      "        [ 8.9324e-01],\n",
      "        [ 8.9887e-01],\n",
      "        [ 9.0435e-01],\n",
      "        [ 9.0968e-01],\n",
      "        [ 9.1487e-01],\n",
      "        [ 9.1990e-01],\n",
      "        [ 9.2478e-01],\n",
      "        [ 9.2951e-01],\n",
      "        [ 9.3409e-01],\n",
      "        [ 9.3850e-01],\n",
      "        [ 9.4277e-01],\n",
      "        [ 9.4687e-01],\n",
      "        [ 9.5082e-01],\n",
      "        [ 9.5462e-01],\n",
      "        [ 9.5825e-01],\n",
      "        [ 9.6173e-01],\n",
      "        [ 9.6504e-01],\n",
      "        [ 9.6820e-01],\n",
      "        [ 9.7119e-01],\n",
      "        [ 9.7403e-01],\n",
      "        [ 9.7671e-01],\n",
      "        [ 9.7923e-01],\n",
      "        [ 9.8159e-01],\n",
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    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2d45f335550>]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 预测结果\n",
    "with torch.no_grad():\n",
    "    res = model(x_tran_input)\n",
    "    print(res)\n",
    "\n",
    "# 画图 预测值和真实值\n",
    "plt.plot(x_train,res)\n",
    "plt.plot(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3a01c7e5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "torch_py38",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
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